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Drug Resistance, Microbial

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Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.

Frontiers in cellular and infection microbiology
The issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) t...

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments.

Water research
The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the an...

Feasibility study of machine learning to explore relationships between antimicrobial resistance and microbial community structure in global wastewater treatment plant sludges.

Bioresource technology
Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistance (AMR) host bacteria in WSs is crucial for understanding AMR formation and mitigating biological a...

Mining biology for antibiotic discovery.

PLoS biology
The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.

Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.

PLoS computational biology
Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacterial infection, the bacteria may be resistant to one or several antibiotics. It has been suggested that...

Preliminary exploration of ChatGPT-4 shows the potential of generative artificial intelligence for culturally tailored, multilingual antimicrobial resistance awareness messaging.

American journal of veterinary research
OBJECTIVE: Antimicrobial resistance (AMR), a global threat driven by factors such as improper antimicrobial use in humans and animals, is projected to cause 10 million annual deaths by 2050. For behavior change, public health messages must be tailore...

Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence.

Environmental pollution (Barking, Essex : 1987)
Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such,...

Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: Insights from metagenomic analyses and machine learning.

Water research
The pervasive presence of microplastics (MPs) in aquatic systems facilitates the transmission of antibiotic resistance genes (ARGs), thereby posing risks to ecosystems and human well-being. However, owing to variations in environmental backgrounds an...

Strategies in using artificial intelligence to combat antimicrobial resistance.

Recenti progressi in medicina
Infectious diseases caused by pathogens resistant to antimicrobial treatments, defined as antimicrobial resistance (AMR), are a serious global health crisis, considered among the main threats to global public health according to the World Health Orga...

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

Bioinformatics (Oxford, England)
SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challen...